US 12,469,251 B2
Coating region learning device, coating region learning method, coating region prediction device, and coating region prediction method
Shinsaku Wakasugi, Hyogo (JP); Takashi Yamade, Hyogo (JP); Naoto Iwashita, Hyogo (JP); and Shin Watanabe, Hyogo (JP)
Assigned to ASICS CORPORATION, Hyogo (JP)
Appl. No. 18/029,877
Filed by ASICS CORPORATION, Hyogo (JP)
PCT Filed Oct. 27, 2020, PCT No. PCT/JP2020/040234
§ 371(c)(1), (2) Date Mar. 31, 2023,
PCT Pub. No. WO2022/091212, PCT Pub. Date May 5, 2022.
Prior Publication US 2023/0274524 A1, Aug. 31, 2023
Int. Cl. G06V 10/44 (2022.01); G06V 10/776 (2022.01)
CPC G06V 10/44 (2022.01) [G06V 10/776 (2022.01)] 18 Claims
OG exemplary drawing
 
1. An application area learning device, comprising:
a learning processor that performs machine learning on a correlation between a feature value at least inside a contour to be extracted from an outline image of a bottom surface of an upper portion of a shoe and an application area of an adhesive in the upper portion of the shoe and generates a prediction model; and
a learned model storage medium that stores the prediction model;
the machine learning including a multilayer neural network and weights of connections between layers of the neural network, the weights learned by deep learning so that an error between application area data of correct answer data and output data output from an output layer of the neural network is minimized;
the minimizing is performed by adjusting the weights of each layer of the neural network so that the error supplied from the output layer is minimized;
the learned neural network by which the error with respect to the feature value within the contour and the application area data has been minimized is stored for estimating the application area from the feature value within the contour extracted from the outline image of an unknown shoe component, in an application area prediction device.